An artificial intelligence feedback loop connects generated material, ratings, clicks, retrieved documents, or real-world outcomes back into future generation or selection. The loop can improve adaptation when feedback reflects genuine quality, but it can also amplify errors and weak proxies.
Research citation loops are dangerous because several model outputs may appear to be independent confirmations while ultimately repeating one unsupported origin. Provenance, deduplication, primary-source checks, and independent evaluation are needed to break the cycle.
